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Claims -- and the Law of Unintended Consequences When Dan Bricklin was a student at Harvard Business School in the late 1970s, he got tired of having to recalculate all the values for the cells in a spreadsheet and realized he could produce an electronic version of a spreadsheet that would run on a personal computer from a little company called Apple. In the process, he didn't just have software take over tedious work that had bedeviled MBA students for generations; he unleashed a wave of innovations far beyond anything he expected. Many attribute to the electronic spreadsheet the wave of mergers and acquisitions that began in the 1980s and never really stopped. With an electronic spreadsheet automatically cascading changes in assumptions through a financial model, analysts could suddenly test any number of possibilities and tweak a model until it justified a takeover or divestiture. The clever analysis that resulted created any number of billion-dollar fortunes and reshaped industries. Private equity couldn't exist in the form it takes today without the electronic spreadsheet. In this month's interview for ITL Focus, my longtime friend and colleague John Sviokla takes us through some of the unintended consequences that digitization could bring to insurance claims. Based on a profound concept known as "computability," he describes a variety of ways in which the ability to capture more information in a form that computers can evaluate and process could cross thresholds that would allow for a host of new business models.  For instance, an insurer could get pictures from an auto accident and not just evaluate the cost of repairs but instantly check the spot markets for used parts and perhaps decide the car is worth more dead than alive. The decision could trigger a series of simultaneous actions -- an offer to buy the car, an offer to replace it with a comparable vehicle located via Carvana and delivered to the person's home and an offer to sell parts to dealers. Some of the concepts are so unfamiliar that they take some processing, but John is always worth listening to. When he was a professor at Harvard Business School, he was the coauthor of an article in Harvard Business Review that laid out the contours of e-commerce in 1994, before most people even knew that a web browser existed. When John and I were partners at Diamond Management & Technology Consultants in the late 1990s and early 2000s, he wrote an article for a magazine I edited, on how the age of ownership was passing away because the internet would allow everything to be rented as needed -- presaging asset-light business models such as those for AirBnB and Uber. John went on to be a senior partner at PwC and is now a partner at Manifold, a venture holding company. I hope you enjoy reading the interview half as much as I enjoyed the conversation with John. And stay tuned. He has promised to keep developing the idea of computability and how it applies across the insurance industry. Cheers, Paul   John Sviokla, a former Harvard Business School professor who has spent decades working at the intersection of technology and strategy, explores the implications of the increased "computability" of insurance claims. He explains how increased digitization not only makes the process more efficient but opens up possibilities for radically new business models.  "There's a tremendous amount of variation in how claims can and should be assessed and handled, but if you have tons of claim data, tons of pictures, structural information, etc., then you basically just have a large numbers problem, and you can compute whatever you need."  -John Sviokla Read the Full Interview READ MORE How Automation Can Streamline Claims Insurance claims software lets agencies reduce fraud, shorten the claims lifecycle, optimize costs, improve efficiency and a lot more. Read More A Wake-Up Call on Claims "The industry is lagging far behind financial services and utilities providers when it comes to the digital customer experience," J.D. Power finds. Read More Optimizing Care with AI Workers Comp Claims  Artificial intelligence and machine learning have held out some hope of heading off problems, but the industry has been cautious about exploring these possibilities.  Watch Now  3 Ways to Cut Time, Frustration, Cost in Accident Claims Embracing a digital approach reduces roadblocks to deliver exponential benefits for policyholders and your claims organization. Read More How to Transform Claims Experience Carriers that transform claims and service operations can turn a cost into a revenue-supporting, loyalty-driving, growth opportunity. Read More It’s Time to Reimagine Digital Payments The acceptance and delivery of payments must be in real time, and capabilities must let customers create tailored digital payment experiences that fit their needs.  Read More FEATURED THOUGHT LEADERS Faheem Shakeel Mubbin Rabbani Brent Williams Karen Furtado Dr. Paul Kim  Nicole Corey   View all ITL FOCUS topics Share Share Tweet

Leadership positions in the insurance industry have been historically dominated by men. But that is starting to change — and it’s a change long overdue. 

Over the past 20 years, I’ve risen through the ranks to become a leader, but it wasn’t easy. I often experienced an industry work culture that reserved management roles for male counterparts even as a number of female colleagues contributed higher-quality work and had a bigger impact on the bottom line. While I was able to succeed in this challenging environment, I recognize that more must be done to level the playing field. I applaud the steps many organizations have taken and current efforts to promote more women into the leadership positions they deserve. But I challenge insurance companies to embrace an inclusive culture that seeks female viewpoints. Otherwise, they stand to miss out on quality leaders and ultimately see their businesses falter.  

Workplace culture needs an overhaul

As an eager and enthusiastic employee early in my career, I worked hard, produced results and was promoted. I became aware that, in large part due to the male-dominant leadership team, I settled into a role where I felt I had to act a certain way that was counter to my authentic self. I focused on what I thought looked good to management as opposed to doing what elevated me to leadership in the first place — doing great work and being an emotionally intelligent person. My instinct was to be a servant leader but this was frowned upon by male and female leaders alike. 

After more than 20 years in this business, I know my experience is common. I’m one of many women who have endured this negative cycle in becoming insurance leaders. In my home country of the U.K., the entrenched workplace culture in insurance sometimes ran rampant — and its side effects cascaded through the entire workforce, women and men alike. 

The data underscores the problem. Research on U.K. insurance workers spotlighted the negative consequences of the country’s insurance workplace culture:

• 50% have witnessed problematic behavior at work 
• 41% have suffered a reduction in productivity because of bad workplace experiences 
• 29% have taken time off from work due to issues such as bullying, harassment or sexual misconduct 

Not only are these side effects damaging to employee morale and well-being, they’re also bad for business: A non-supportive workplace culture is 10x more likely to make employees quit, and a less engaged and absent workforce can cost U.K. employers up to £36 billion annually.  

While I have navigated this pervasive workplace culture and am committed to helping highlight the deficiencies and bring about change, many other women grow tired and seek employment in other industries. And the numbers show the impact of these women dropping out — only 30% of vice president positions in the insurance industry are represented by women, and just 18% of C-suite positions are occupied by women. 

The bottom line: We cannot tolerate the status quo. It’s detrimental to employees’ well-being, and it discourages women from aspiring to leadership positions.

Change the narrative by changing culture

What can insurance companies do to break the chain of long-standing toxic industry culture and better support women as leaders? 

We’re only at the beginning stages of shifting the insurance industry norm to a healthier, more inclusive workplace culture. By accelerating that shift at your organization, you can lay a foundation for a positive workplace environment, help champion female leaders and achieve better business outcomes.

Here are a few things your organization can do to bridge the leadership gap for women in insurance and create a better workplace culture: 

• Practice servant leadership. Although established leaders are important for organizational direction, it’s also important to decentralize your culture with servant leadership — a leadership style that encourages collaboration between senior and junior staff and allows people to speak up. For example, I often ask direct questions of our CEO at Amwins. Our organization encourages collaboration from the entry level all the way to the C-suite. This type of culture encourages employees to be themselves and specifically lets women know that they don’t need to become someone else to succeed. 
• Provide diverse leadership programming. I realized years ago that I no longer wanted to lead the way I had learned from executives with whom I worked. So, I took it upon myself to find learning and development (L&D) programs that taught leadership styles that align with my personal values. You can enable your employees to become leaders on their own terms by offering L&D leadership programs that cater to different leadership styles. A variety of L&D offerings is especially key for women who may have been discouraged from practicing different leadership styles in the past. 
• Democratize career growth paths. Too often, I saw other leaders around me who were given opportunities mostly for how they looked in the company’s eyes rather than their actual work contributions. Many times, I felt that I and others — especially women I worked with — were better suited for leadership positions because of our work merit, not just our image. This is why it’s critical to clearly define what growth looks like in your organization so leadership opportunities are fairly given to all employees and women have a truly level playing field. An added payoff is that organizations that focus on gender inclusion and prioritize the advancement of women report up to 61% higher revenue growth than other companies. 

Take the insurance industry into a new age 

We’re entering a new and better age for women in the insurance industry. But, to enter this new era, change needs to happen. By building a workplace environment on servant leadership, investing in robust L&D programs and clearly defining career paths, your organization can be a champion of women in an industry that has historically worked against them.

Over the last five years, the risks insurance tackles and the people it protects have undergone a  systemic shift. In such an intense period of change, the race is truly underway for insurance companies to keep up with the most innovative technologies and rising customer expectations – all while operating in an increasingly unpredictable risk environment. Insurers must move faster than ever to execute ambitious transformation plans and innovate with customer-centric products to not only gain market share but ultimately retain it profitably. 

That's why we're excited that in-person events are making a comeback this year. With all the complex challenges and opportunities facing the industry, there is real benefit to meeting face to face.

As Matteo puts it: “As a speaker, I need to feel the  energy of the people in the room and to look at their eyes while I’m presenting. Also, quality networking requires an onsite presence. You build quicker (and better) relationships with people who are physically in front of you, than virtually. I see one more fundamental reason for going back to the in-person format, too: the depth and breadth of the lessons learned.” 

In today’s fast-moving market, with unsettled customer behaviors and trends, rapidly adapting to change is fundamental not only for thriving but even for surviving. Reuters Events exists to deliver the intelligence and foster the relationships that shape strategy and secure growth for leading insurers worldwide – and there’s no better way to do this than in-person.  

Each of us have reflected on the personal experience and identified at least three connected reasons why in-person conferences are better for learning: 

• The Commitment: When you decide to attend an in-person event, you are investing a  significant amount of your time. You keep your diary clear all day; the only placeholders from the outside world are truly unavoidable things. Moreover, you trust the editor who curated the content, and you attend most of the sessions on the agenda. However, at virtual  events, where computer fatigue is a real thing, you don’t block out the calendar and listen to each session – instead opting to attend a few sessions that you have pre-evaluated as important for you, therefore limiting your experience to solely familiar content. You likely leave the online platform just after you ingested the content, leaving you without interaction and exposure to lateral thinking. On the flipside, at an in-person conference you spend time hearing from and talking with strangers, and you feed your brain with their alternative points of view and out-of-the-box ideas. 
• The Interaction: To speak in front of a cup of coffee with the speaker and some of the attendees allows you to get the color that exists around a topic. Everything seems easy on the polished slides presented, and online you are only scraping the surface of an insurance innovation initiative. It is only when talking with those who have directly experimented and tried new ideas that you can understand what happens behind the scenes. The interactions between the brains of the in-person attendees generates knowledge that no one of them would have obtained alone. 
• With an on-site presence, you are there 100%. The contents of the conference and the participants around you come first. When you are attending remotely from your home, you have many sources of distraction from your partner to your pet, and  even your fridge! There’s always that presentation to finish, and that email backlog to address. All these forces compete – together with the notifications on your phone – for your attention and limit your focus on learning.  

We are looking forward to catching up with you in-person at events in the coming months. 

This article was co-written by Alexandra Wilson, insurance project director at Reuters Events, and Matteo Carbone, director of the IoT Insurance Observatory and board member at Net Insurance. For more information or to get involved in Reuters events, email Alexandra.Wilson@thomsonreuters.com.

In early 2022, Enlyte conducted a survey of workers’ compensation professionals to understand how the industry’s use of technology changed over the past year. The results demonstrated that COVID-19 is still a major driver of technology adoption in the industry today. Most payers are focused on applying technology to solve, or at least ease, COVID-related pain points, from managing unpredictable claim patterns to dealing with staff changes due to "the Great Resignation."

Looking ahead five to 10 years, the survey indicated payers will place greater emphasis on technology to create a better claims experience for injured employees, such as by improving communication and focusing on the return-to-work process. 

Additionally, the survey found that there is still a significant opportunity for payers to increase straight-through processing of medical bills, because only a few respondents said they process more than half of their medical bills automatically. 

Survey Results

Here were some of the most interesting results from Enlyte’s 2022 workers’ compensation technology survey:  

1. Today, payers are focused on using technology to improve efficiency and help solve COVID-19 challenges.

• In 2021, the industry invested most in initiatives to implement telemedicine, electronic payments and billing.
• Of all respondents, 27% said COVID-19 claims challenges, specifically claim volumes and types of claims, are the biggest drivers of technology adoption in the workers' compensation industry today, followed by a need to increase efficiency.
• Those who said their organizations implemented new technologies in 2021 indicated that artificial intelligence and improved user experience and integrations made the biggest impact on their businesses.

One of the biggest trend changes revealed in Enlyte’s 2022 survey was the growing number of respondents who said their companies invested in electronic payments last year. Electronic payments and billing moved from fourth place in the 2021 survey to second in 2022. This demonstrates how payers have been working to implement technologies to support virtual workforces and modernize their processes. Additionally, electronic payments and billings can provide cost savings. Paying medical bills via paper check can cost between $4 and $30 per transaction — an electronic payments program can reduce that cost by up to 90%. 

2. In the next five to 10 years, the industry will focus on using technology to improve injured employees' experiences. 

• Of all respondents, 22% said mobile apps and other communication technologies are expected to have the most influence on the industry in the next five to 10 years.
• Additionally, 28% of respondents believe communicating with injured employees is the step in the claim lifecycle that could benefit most from applying new or additional technology.

While implementing new technologies in this area can provide significant benefits to all stakeholders in the claims process, it is important that the industry strikes the right balance by focusing on technologies with a low entry barrier, so injured employees will actually use them. For example, while mobile apps may be convenient, finding investment dollars and resources to build them and convincing people to download them can be challenging. Instead, it may be more effective and efficient for all parties involved if a payer focused on improving communication with technologies people already use in their everyday lives, like text messaging or email. 

3. The workers’ compensation industry has an opportunity to increase automation.

• Only 21% of respondents said their organization is automating more than 50% of their bills using straight-through automation.
• 32% responded that their organization is only automating 0-5% of workers’ compensation claims. 
• About half (48%) of respondents said their company uses a homegrown claims system, which can allow payers to create extensive customization but may cause them to miss out on opportunities to increase automation.

It is easy for payers to increase straight-through automation using business rules engines and other technologies they already have in place. For instance, a high-powered bill review rules engine can facilitate up to 90% straight-through processing rates. The survey revealed a clear opportunity for many payers to take a closer look at automating many of their medical bills for more consistent and efficient outcomes. 

Understanding the Future of Technology in Workers’ Compensation

The survey results show an industry interested in applying technologies to not only react to major trends and changes but also to improve the experience for its main constituents – injured employees. In the near term, we expect workers’ compensation will continue to focus heavily on improving efficiencies, getting a new generation of employees up to speed fast and creating overall better processes internally. Once organizations have laid a solid internal foundation, then we will see focus shift to the return-to-work process, improving injured employee communication and more. Overall, all of these technology changes should lead to improved claims processes and overall better outcomes for all parties. 

Enlyte surveyed 115 workers’ compensation professionals in January and February 2022. Most survey respondents have a role at their company at the manager level or higher and work for an insurance carrier, third party administrator or employer. About 40% said they have been working in the workers’ compensation industry for more than 15 years, and about half work for a company with 1,000 employees or fewer.

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AI's Cool New Trick Paul Carroll, Editor-in-Chief of ITL One of my lingering misgivings from my days as a reporter at the Wall Street Journal concerns the role I played in unleashing PowerPoint on the world.  I was just doing my job. Honest. Like all good journalists, I was at the bar, at a conference in the spring of 1987, when a consultant I knew introduced me to one of the two founders of a startup that was about to unveil PowerPoint. The demo he showed me was intriguing, and the prospects for the product checked out with a number of smart folks I interviewed at the conference, so I wrote about it.  Next thing you know, Microsoft buys the startup, and we're all awash in bullet points and so many fonts and type sizes that presentations may look like ransom notes. Today, I'd like to introduce you to the latest advancement in visual presentations, an artificial intelligence that lets you generate an image based on a single sentence -- for instance, the AI produced the image above based on the prompt, "astronaut riding a horse in a photorealistic style."  I can't guarantee that problems won't eventually arise with this technology, too, but at least it'll be a lot more fun than fussing with PowerPoint templates.  continue reading > Podcast Alert Join Denise Garth for a new podcast featuring Deloitte's Abhishek Bakre, Senior Manager of Strategy and Santosh Kutty, Principal, to discuss changing customer demographics, absence management, and bundling with disability and leave products. Listen Now SIX THINGS In Competition for Top Talent, Innovation Matters by Tom Warden People want to do work that is interesting and purposeful. They value an organizational culture that places a premium on creativity, thought leadership and innovation. Read More How to Reduce Distracted Driving by Adam Pichon Few drivers are aware that texting while traveling at 55mph is the equivalent of driving the length of a football field with your eyes closed. Read More The Future of Work  Wednesday, May 4th at 1:00 pm EDT Sponsored by JobsOhio  As employees start to return to work after two years of mostly working remotely, smart employers are rethinking just about all aspects of how work is done to get the best of both the home and office worlds.  Register Today How to Preempt Disinformation by Vivek Wadhwa Just as the U.S. spends hundreds of billions on warships and planes, it should invest in an information warfare machine focused on continually preempting disinformation. Read More Top Brokers' Advantage by L.S. Ram For brokerages that intend to participate in fast-emerging opportunities, the future is now. It’s time to carpe diem.  Read More Time to Embrace AI in Climate Change Fight by Andrew Yeoman We often think about climate change in the long term. It's easy to forget that we already live with the consequences of unchecked climate change today. Read More Lessons From Travel Insurance by Ryan Brubaker When done correctly, technology is as transparent as it is critical. When implemented poorly, it's like begging your customers to look elsewhere for services. Read More Oliver Wyman: Reset4Value Series  Drive the next wave of growth  Sponsored by Oliver Wyman Oliver Wyman’s Reset4Value approach helps insurers transform cost and ignite growth. Our insight shares how leaders can leverage their firm’s culture strengths, enhance the capabilities that matter most, and unlock scarce investment dollars to fund them appropriately. Read Now  MORE FROM ITL April Focus: Automation and RPA Sometimes, innovation takes time.   Some 30 years ago, I wrote an article for the front page of the second section of the Wall Street Journal that declared a revolution in forms. We were far enough along in the personal computer revolution that software companies were coming out with products that would let users fill out forms on-screen, speeding the process and eliminating the errors that occurred as someone had to interpret people's handwriting. Even more magical, the spread of local area networks meant that information could flow straight from my screen into a corporate database, with no never to ever print the form and have someone re-enter the data. .  Everything I wrote was correct, and forms did take a major step forward, but, here we are three decades later, still drowning in forms. And the insurance industry is Exhibit A.  Read More   Winning the War for Talent  Thursday, April, 28th at 1 pm EDT Sponsored by PwC This webinar tackles a key issue -- maybe the key issue -- facing the insurance industry: How can we attract, train and retain the talent that we need and that the industry's mission merits. Watch Now    Partner with ITL to create expert thought leadership content. Custom Content Promoted Content Display Advertising Custom Webinars Monthly Topic Sponsorships ITL Partner Packages and more Learn more and get the 2022 Media Kit

Artificial intelligence (AI) and machine learning (ML) are transforming the insurance industry. Many companies are already using them to assess underwriting risk, determine pricing and evaluate claims. But, if the proper guardrails and governance are not put into place early, insurers could face legal, regulatory, reputational, operational and strategic consequences down the road. Given the heightened scrutiny surrounding AI and ML from regulators and the public, those risks may come much sooner than many people realize.

Let's look at how AI and ML function in insurance for a better understanding of what could be on the horizon.

A Quick Review of AI and Machine Learning

We often hear the terms "artificial intelligence" and "machine learning" used interchangeably. The two are related but are not directly synonymous, and it is important for insurers to know the difference.

Artificial intelligence refers to a broad category of technologies aimed at simulating the capabilities of human thought.

Machine learning is a subset of AI that is aimed at solving very specific problems by enabling machines to learn from existing datasets and make predictions, without requiring explicit programming instructions. Unlike futuristic "artificial general intelligence," which aims to mimic human problem-solving capabilities, machine learning can be designed to perform only the very specific functions for which it is trained. Machine learning identifies correlations and makes predictions based on patterns that might not otherwise have been noted by a human observer. ML's strength rests in its ability to consume vast amounts of data, search for correlations, and apply its findings in a predictive capacity.

Limitations and Pitfalls of AI/ML

Much of the potential concern about AI and machine learning applications in the insurance industry stems from predictive inference models - models that are optimized to make predictions primarily or solely on correlations in the datasets, which the models then employ in making predictions. Such correlations may reflect past discrimination, so there is a potential that, without oversight, AI/ML models will actually perpetuate past discrimination moving forward. Discrimination can occur without AI/ML, of course, but the scale is much smaller and therefore less dangerous.

Consider if a model used a history of diabetes and BMI as factors in evaluating life expectancy, which in turn drives pricing for life insurance. The model might identify a correlation between higher BMI or incidence of diabetes and mortality, which would drive the policy price higher. However, unseen in these data points is the fact that African-Americans have greater rates of diabetes and high BMI. Upon a simple comparison of price distribution by race, these variables would cause African-Americans to have higher pricing.

A predictive inference model is not concerned with causation; it is simply trained to find correlation. Even when the ML model is programmed to explicitly exclude race as a factor in its decisions, it can nevertheless make decisions that lead to a disparate impact on applicants of different racial and ethnic backgrounds. This sort of proxy discrimination from ML models can be far more subtle and difficult to detect than the example outlined above. They also might be acceptable, as in the prior BMI/diabetes example, but it is critical that companies have visibility into these elements of their model outcomes.

There is a second major deficiency inherent in predictive inference models, namely that they are incapable of adapting to new information unless or until they are properly acclimated to the "new reality" by training on updated data. Consider the following example.

Imagine that an insurer wishes to assess the likelihood that an applicant will require long-term in-home care. They train their ML models based on historical data and begin making predictions based on that information. But, a breakthrough treatment is subsequently discovered (for instance, a cure for Alzheimer's disease) that leads to a 20% decrease in required in-home care services. The existing ML model is unaware of this development; it cannot adapt to the new reality unless it is trained on new data. For the insurer, this leads to overpriced policies and diminished competitiveness.

The lesson is that AI/ML requires a structured process of planning, approval, auditing, and continuous monitoring by a cross-organizational group of people to successfully overcome its limitations.

See also: Eliminating AI Bias in Insurance

Categories of AI and Machine Learning Risk

Broadly speaking, five categories of risk related to AI and machine learning exist that insurers should concern themselves with: reputational, legal, strategic/financial, operational, and compliance/regulatory.

Reputational risk arises from the potential negative publicity surrounding problems such as proxy discrimination. The predictive models employed by most machine learning systems are prone to introducing bias. For example, an insurer that was an early adopter of AI recently suffered backlash from consumers when its technology was criticized due to its potential for treating people of color differently from white policyholders.

As insurers roll out AI/ML, they must proactively prevent bias in their algorithms and should be prepared to fully explain their automated AI-driven decisions. Proxy discrimination should be prevented whenever possible through strong governance, but when bias occurs despite a company's best efforts, business leaders must be prepared to explain how systems are making decisions, which in turn requires transparency down to the transaction level and across model versions as they change.

Key questions:

  1.  In what unexpected ways might AI/ML model decisions impact our customers, whether directly or indirectly?
  2.  How are you determining if model features have the potential for proxy discrimination against protected classes?
  3.  What changes have model risk teams needed to make to account for the evolving nature of AI/ML models?

Legal risk is looming for virtually any company using AI/ML to make important decisions that affect people's lives. Although there is little legal precedent with respect to discrimination resulting from AI/ML, companies should take a more proactive stance toward governing their AI to eliminate bias. They should also prepare to defend their decisions regarding data selection, data quality, and auditing procedures that ensure bias is not present in machine-driven decisions. Class-action suits and other litigation are almost certain to arise in the coming years as AI/ML adoption increases and awareness of the risks grows.

Key questions:

  1.  How are we monitoring developing legislation and new court rulings that relate to AI/ML systems?
  2.  How would we obtain evidence about specific AI/ML transactions for our legal defense if a class-action lawsuit were filed against the company?
  3.  How would we prove accountability and responsible use of technology in a court of law?

Strategic and financial risk will increase as companies rely on AI/ML to support more of the day-to-day decisions that drive their business models. As insurers automate more of their core decision processes, including underwriting and pricing, claims assessment, and fraud detection, they risk being wrong about the fundamentals that drive their business success (or failure). More importantly, they risk being wrong at scale.

Currently, the diversity of human actors participating in core business processes serves as a buffer against bad decisions. This doesn't mean bad decisions are never made. They are, but as human judgment assumes a diminished role in these processes and as AI/ML take on a larger role, errors may be replicated at scale. This has powerful strategic and financial implications.

Key questions:

  1.  How are we preventing AI/ML models from impacting our revenue streams or financial solvency?
  2.  What is the business problem an AI/ML model was designed to solve, and what other non-AI/ML solutions were considered?
  3.  What opportunities might competitors realize by using more advanced models?

Operational risk must also be considered, as new technologies often suffer from drawbacks and limitations that were not initially seen or that may have been discounted amid the early-stage enthusiasm that often accompanies innovative programs. If AI/ML technology is not adequately secured - or if steps are not taken to make sure systems are robust and scalable - insurers could face significant roadblocks as they attempt to operationalize it. Cross-functional misalignment and decision-making silos also have the potential to derail nascent AI/ML initiatives.

Key questions:

  1.  How are we evaluating the security and reliability of our AI/ML systems?
  2.  What have we done to test the scalability of the technological infrastructure that supports our systems?
  3.  How well do the organization's technical competencies and expertise map to our AI/ML project's needs?

Compliance and regulatory risk should be a growing concern for insurers as their AI/ML initiatives move into mainstream use, driving decisions that impact people's lives in important ways. In the short term, federal and state agencies are showing an increased interest in the potential implications of AI/ML.

The Federal Trade Commission, state insurance commissioners, and overseas regulators have all expressed concerns about these technologies and are seeking to better understand what needs to be done to protect the rights of the people who live under their jurisdiction. Europe's General Data Protection Regulation (GDPR), California's Consumer Privacy Act (CCPA), and similar laws and regulations around the world are continuing to evolve as litigation makes its way through the courts.

In the longer term, we can expect regulations to be defined at a more granular level, with the appropriate enforcement measures to follow. The National Association of Insurance Commissioners (NAIC) and others are already signaling their intentions to scrutinize AI/ML applications within their purview. In 2020, NAIC released its guiding principles on artificial intelligence (based on principles published by the OECD) and in 2021, created a Big Data and Artificial Intelligence Working Group. The Federal Trade Commission (FTC) has also advised companies across industries that existing laws are sufficient to cover many of the dangers posed by AI. The regulatory environment is evolving rapidly.

See also: Time to Embrace AI in Climate Change Fight

Key questions:

  1.  What industry and commercial regulations from bodies like the NAIC, state departments of insurance, the FTC, and digital privacy laws affect our business today?
  2.  To what degree have we mapped regulatory requirements to mitigating controls and documentary processes we have in place?
  3.  How often do we evaluate whether our models are subject to specific regulations?

These are all areas we need to watch closely in the days to come. Clearly, there are risks associated with AI/ML; it's not all roses when you get beyond the hype of what the technology can do. But understanding these risks is half the battle.

New solutions are hitting the market to help insurers win the risk war by developing strong governance and assurance practices. With their help, or with in-house specialists on board, risks will be overcome to help AI/ML reach its potential.

As first published in Dataversity.

ITL:

I’m reading a fascinating book called “The Dream Machine” about the very early days of computing, and the idea of “computability” was phenomenally powerful even back in the 1930s, ‘40s and ‘50s. You’ve updated the idea considerably and are applying it to insurance, among other areas. Let’s start by having you spend a couple of minutes laying out what “computability” means today.

John Sviokla:

Businesses are finding that they can take known algorithms and capabilities of mathematics and apply them at whole new levels. There are all kinds of insurance now where the statistics that are being used to price a $500 insurance policy are as good as what was used for a multimillion-dollar policy 40 years ago. That’s simply because of the availability of the data plus the low cost of using the algorithms to make that happen. You've taken something that used to be based on judgment, because of a combination of lack of data and high cost, and made it computable.

And there are lots of implications. For instance, businesses can now do mirroring – setting up a fully digital, detailed model of their business or major parts of it so they can experiment with changes before implementing them in the physical world. And that mirroring allows for really profound changes because you’ve created a symbolic world that can exist even in and of itself. Basically, you can have algorithms talking to algorithms via robots.

You can think of market slices not in terms of geography but in terms of points in time. That’s a whole new dimension of a market that's being born in a symbolic space that is only accessible to the computable decision that can operate in billionths or millionths of a second. You’re creating these limits, almost like little Russian dolls in time. Every second expands out into tradable realities in the context of these symbol systems.

One last part is sensors.

Generally speaking, one way to look at how these symbol systems relate to reality is that they are representation systems. What is a Chinese character? It's an abstraction as part of a representation system, like the sounds we make on the phone that distill reality and communicate.

What's happening with IoT is a lot like what happened with the Gutenberg press. People had read before, but the Gutenberg revolution expanded existing representation systems called language and books. We're seeing another vast expansion with all these sensors that can help us understand and represent the world digitally.

And neural networks are incredibly powerful at taking continuous variables measured by these sensors and making them discrete. Variables that used to be analog can now be registered in digital form, where AI can unleash all kinds of advances.

ITL:

I think we can start to imagine some of the implications for insurance.

Sviokla:

This move from continuous to discrete variables can be a profound starting point for claims. We gather all kinds of new, continuous variables, and you can create a whole new representation system that vastly improves your decision making.

You can also build on your new access to computable data and look at your business in new dimensions. Am I just fixing this Toyota? Am I fixing this Toyota Camry today after it's been hit? Am I looking at the whole population of Toyotas? Am I contracting with service centers across the country to do a continuous service level agreement that keeps all Toyotas in the U.S. up and running?

You get so much granularity that you can act like the capital markets, where you can trade the bond or the mortgage-backed security or a portfolio of securities or a synthetic CDO or whatever. In insurance, you can “trade” that Toyota Avalon crash or trade Avalon bumpers or trade the parts that go into the bumpers or whatever, including in derivative markets based on all those physical assets. The key is to have the syntax and semantics of trading, which computability allows you to do.

ITL:

Let’s drill down into claims, as long as you’ve cited that as a key area where computability can transform insurance.

Sviokla:

There's a tremendous amount of variation in how claims can and should be assessed and handled, but if you have tons of claim data, tons of pictures, structural information, etc., then you basically just have a large numbers problem, and you can compute whatever you need. You can then drive toward more and more diagnostic specification accuracy, lower labor costs. quicker turnaround and higher quality. You can also do a better job of managing claims at the population level.

ITL:

Are we primarily doing a better job of computing what we already know, or is computability also taking us into unexplored territory?

Sviokla:

The majority in the near term will be in computing better what's already known. That, alone, will have a massive effect on the productivity of claims agents and allow for the least expensive provision of service.

Over time, at the more aggregate level, you'll start to see a combination of different variables that will begin to inform us about where accidents happen – what intersections, which drivers, what time of day, what conditions and so on. I think that's going to be more longer-term because the issue doesn't sit in anybody's wheelhouse, in particular. Each piece of that value equation is spread out.

ITL:

I’ve long been a proponent of the idea that we need to sell protection and not just indemnify people after a loss. Nobody wants to buy insurance, but everybody wants to be protected.

Sviokla:

I think we're going to need a couple of generations of smart cars. A car is a moving powerplant with sensors and a human, right? And the number of those sensors is going to continue to go up, and the intelligence is going to continue to go up in the car. Those capabilities will birth all kinds of new computable reality, and there’s a threshold effect. At some point, some clever person at Tesla or maybe Mercedes is going to say, buy a car from us and we will give you road intelligence based on the sensors in all our cars, plus NASA, plus blah, blah, blah. And that's real-time road intelligence, which will reduce claims.

Everything about a claim will be informed by the digital representation of how someone has taken care of a vehicle. So, did you take good care of the truck? Were you driving too fast? Where are the parts from, such as the brakes? Did they work the way they're supposed to in this condition? The claim becomes a punctuation point in the context of a continuous analysis.

The general argument is that you have isomorphisms among different representations of reality. I mean, think of the design of a building. In the old days, you had an outside drawing, then the engineering drawing and construction drawing and contractors’ blueprints. Then you have the “as built” and maintenance documentation on top of that. Those are all projections of one reality across time and function. What you have the ability to do now is to have one reality, which then gets projected for each audience in each need across time.

Computability will make all kinds of things easier and open up all sorts of funky possibilities. Maybe somebody files a claim, and you say, we’ll just buy the car from you, get you a comparable model from Carvana and deliver it to you. You already know what the resale value is for the car you’re offering to buy. You might even have it resold before you buy it. Maybe you’re in the parts market and are getting contingent offers on the car you’re buying.

Computability creates a possibility cloud around every asset.

ITL:

Thanks, as always, John

One of my lingering misgivings from my days as a reporter at the Wall Street Journal concerns the role I played in unleashing PowerPoint on the world. 

I was just doing my job. Honest. Like all good journalists, I was at the bar, at a conference in the spring of 1987, when a consultant I knew introduced me to one of the two founders of a startup that was about to unveil PowerPoint. The demo he showed me was intriguing, and the prospects for the product checked out with a number of smart folks I interviewed at the conference, so I wrote about it. 

Next thing you know, Microsoft buys the startup, and we're all awash in bullet points and so many fonts and type sizes that presentations may look like ransom notes.

Today, I'd like to introduce you to the latest advancement in visual presentations, an artificial intelligence that lets you generate an image based on a single sentence -- for instance, the AI produced the image above based on the prompt, "astronaut riding a horse in a photorealistic style." 

I can't guarantee that problems won't eventually arise with this technology, too, but at least it'll be a lot more fun than fussing with PowerPoint templates. 

The AI was developed by OpenAI, a nonprofit company backed by Microsoft, among many others, that has the very serious goal of developing what's known as artificial general intelligence -- basically, AI that works broadly, like the human brain, rather than being finetuned for a specific task, such as playing chess or recognizing images. OpenAI has produced an intriguing series of highlights, including defeating a professional e-sports world championship team on a livestream and solving Rubik's Cube with a robot hand. 

But I'm mostly intrigued by the playfulness that becomes possible for presentations via OpenAI's latest development, which is known as DALL-E 2. 

At the risk of telling you more than you want to know about my twisted sense of humor, here are the two images generated by DALL-E 2 that I've enjoyed most so far:

That was generated based on the phrase, "medieval painting of complaining that the Wi-Fi isn't working."

And:

"Ancient Egyptian painting depicting an argument about whose turn it is to take out the trash."

Oh, okay, two more:

"Photo of a grizzly bear confused in calculus class."

And:

"Leonardo enters the metaverse."

I can imagine that the ability to just conjure up an image could contribute to the growing problems for deep fakes -- though the OpenAI folks are also working on ways to make sure AI is used only ethically. So, for now, I'll just let my imagination enjoy itself.

Cheers,

Paul

How do you think the insurance industry is going to look in 10 years? With online-only companies becoming more popular and more desirable, they’re causing the entire industry to rethink how they’re going to stay relevant. The introduction of artificial intelligence into the policy recommendation process is just another piece in the puzzle of how businesses and families will interact with insurance companies 10 years from now. 

Some thoughts to keep in mind:

--Accenture's global Insurance Consumer Study found that 57% of consumers said that they would find it appealing to get advice from their insurer or bank on how to travel and shop more sustainably. Millennials and Zoomers showed even more interest, at 67%. 

--As of 2020, a study conducted by the AnitaB.org Institute found that women make up only 29% of the tech workforce. Another study published by the World Economic Forum found that only 22% of AI professionals across the world are female. These low numbers help contribute to AI systems that show bias. In the next 10 years, women and other diverse members are going to be essential at making sure the industry can identify biases in data before they make it to the consumer.

--Usage-based insurance (UBI) has been gaining traction in the U.S. at a compound annual growth rate estimated above 29% from 2019-2026. 

“Policy holders expect more out of their insurance when it comes to the overall experience. Artificial intelligence isn’t just for smartphones and video games but can also be used to keep us safe and protected and save money. Over the next several years, we'll see AI play an ever-increasing role in the recommendation and accuracy of policy coverages, while also bringing the entire insurance industry into the 21st century to be on par with organizations that offer superb services,“ says Linh C. Ho, chief marketing officer at Zelros.

In recent years, there has been a shift in the world of business, from a focus on transactions to an emphasis on relationships. Today’s companies no longer shortsightedly prioritize profits over everything else but are instead increasingly concerned with earning their consumers’ trust. Instead of simply selling consumers products, they market themselves as companies to believe in and make part of one’s life.

While this trend is evident across the business spectrum, from banking to footwear, there are some industries that should be particularly attentive to such changes and become especially in tune with the needs and wants of its customers—such as insurance. Insurance, after all, represents a relationship with policyholders and their beneficiaries, a sustained commitment to supporting them through tough times. 

It is clear, however, that the financial support provided by insurance payouts, while an incredibly important lifeline to so many families, is not nearly enough in today’s relationships-centered marketplace. If insurers want to address the true needs of their customers, they need to think outside the box and provide added value. 

Insurers’ customers are not just case numbers; they are human beings. And, when insurance companies acknowledge the unique needs and challenges of those they serve, often at some of the hardest times in their lives, the companies benefit themselves, as well.

Insurers can go beyond the payout

So what can insurers do to establish trust with and care for those they support?

In treating their interactions with customers not as sales but as sustained relationships, insurance companies can go beyond payouts to provide beneficiaries with real peace of mind. For example, Sensa, a car insurance company, automatically calls emergency services so that their customers have one less thing to worry about. Some car insurers have also begun offering rental reimbursement coverage, going an extra step to make sure the customer isn’t stuck without transport while waiting for claims or repairs.  

Other insurance companies have incorporated clear social impact goals directly into their offerings, showing customers that their beliefs and commitments are as important to them as their financial security. Insurer Lemonade has implemented a giveback program, charging a flat fee and then giving the leftover money from claims to a charity of the customer’s choice. 

See also: Focus on Evolution, Not a Revolution

Helping with the hidden costs of death

Out of the various insurance verticals, however, the one that most needs to implement such policies is life insurance. Life insurance establishes a relationship over the long term, with a promise to be there for the policyholder’s beneficiaries when they need it the most, when they are experiencing the repercussions of great loss.

Bereaved families often experience not only grief but fatigue and frustration from dealing with complex bureaucratic processes, anxiety and stress from the added financial burden and the potential for even more, including strained relationships and troubled communication. 

In a recent survey of these families, we identified some of the most pressing issues they face. In total, all the logistics around a loss, including planning the funeral, paying bills and taxes, filing the will and handling probate, took an average of 420 hours, or the equivalent of 10.5 full weeks of work.

This is an area where insurers can show up for their beneficiaries in a big way. These burdens are so tough that alleviating them even a little can improve someone's outlook and peace of mind. Providing guidance, resources and tools to help beneficiaries navigate all the complexities of loss, from funeral planning to estate administration, as a supplement to insurance payouts is therefore a relatively simple and low-cost way to cement the trust and loyalty of insurance customers and their beneficiaries.

Conclusion

This new world of deepening reciprocity between businesses and consumers allows us to imagine a new role for insurance, one that makes good on the promise of what is, for many of us, one of the longest relationships we will have with a corporation -- a connection that doesn’t simply involve cash payouts but that directly addresses the situation of the families whose claims it is paying.  

It’s the kind of pairing we expect to see more and more of in the years to come: a relationship that goes beyond the transaction, to bring families not just the money they need at a difficult time but care and support through all the things they need it for.